A new non-interactive K-nearest neighbor classification method under privacy preservation

Abstract

The invention discloses a new non-interactive K-nearest neighbor classification method under privacy protection, invention relates to the vector classification field of K nearest neighbor classification algorithm under privacy protection. The steps are as follows: 1. The client end encrypts the training data in the training data set composed of a plurality of training data with labels by a vectorhomomorphic encryption method to obtain a ciphertext data set and an intermediate matrix, and uploads the ciphertext data set and the intermediate matrix to the cloud; 2. That client end receives theplaintext vector group to be classified and encrypt the plaintext vector group to obtain the ciphertext vector group, and uploads the ciphertext vector group to the cloud end; 3. According to that ciphertext data set and the intermediate matrix, the cloud end calculates the similarity between each ciphertext vector in the ciphertext vector set and all ciphertext data contain in the ciphertext dataset, obtains the classification result set of the ciphertext vector set according to the nearest neighbor classification algorithm, and sends the classification result set to the client end. The invention greatly improves the efficiency and security of encryption, realizes non-interactive technology, achieves real outsourcing calculation, and reduces the calculation pressure of the client.

Description

technical field [0001] The invention relates to the field of non-interactive vector classification of a K-nearest neighbor classification algorithm under privacy protection, in particular to a new non-interactive K-nearest neighbor classification method under privacy protection. Background technique [0002] K-Nearest Neighbor Classification Algorithm is a statistical analysis method for studying classification problems, an important algorithm for data mining, and one of the simplest machine learning algorithms. The input of the K-nearest neighbor classification algorithm is the feature vector of the instance, which corresponds to a point in the high-dimensional feature space. The K-nearest neighbor classification algorithm is based on vector similarity, which is the distance measure between vectors, and generally uses Euclidean distance. The output is the category of the instance, which can be multi-category. The K-nearest neighbor classification method assumes that a trai...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is: in order to solve the K-nearest neighbor classification method, after data is migrated to the third-party cloud, it is easy to cause data privacy leakage, and it needs to interact with the client, and the calculation result is sent back to the client for decryption, which cannot be completely outsourced. The problem of heavy computing burden on the client is provided, and a new non-interactive K-nearest neighbor classification method under privacy protection is provided. features

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